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Biomedical subjects

J S Barlow

Publications and source records attributed to J S Barlow.

At least 19 recordsLinked to original sources
PubMed1998

8-Aminoquinolines as anticoccidials--II.

During a chemistry program aimed at finding a novel analogue of pentaquine with improved in vivo activity, a number of hypotheses concerning the way this drug acts in the chicken were investigated. Consideration of the products of monoamine oxidase metabolism of pentaquine suggested that pentaquine aldehyde is the likely active metabolite. Although isolation of this unstable compound was not possible, oxime and cyclic acetal and ketal derivatives were obtained and shown to possess in vitro anticoccidial activity.

Aminoquinolines↗
PubMed1990

Changes in EEG mean frequency and spectral purity during spontaneous alpha blocking.

Spontaneously occurring brief periods of lower voltage irregular activity occurring amid a background of alpha activity (i.e., alpha blocking) in eyes-closed resting occipital EEG recordings from 32 healthy human subjects have been investigated to determine the extent of changes of mean frequency and of spectral purity (degree of regularity/irregularity of the EEG activity) during such periods. New methods for determining mean frequency and spectral purity (the latter as a new measure, the Spectral Purity Index, which has a maximum value of 1.0 for a pure sine wave) permit their conjoint evaluation over a 0.5 sec window that is advanced along the EEG in 0.1 sec steps, thus permitting almost continuous feature extraction. The findings indicate that, although spectral purity invariably decreased during the periods of lower voltage irregular activity, the mean frequency remained relatively unaltered, i.e., it remained unchanged or it increased or decreased slightly but at most by 2.5 Hz. These results suggest that, at least for the periods of lower voltage irregular activity occurring spontaneously amid an alpha background during eyes-closed occipital EEG recordings, it may be inaccurate (as some authors have already suggested) to use the term 'low-voltage fast (or beta) activity.'

Adult↗
PubMed1985

Computer characterization of tracé alternant and REM sleep patterns in the neonatal EEG by adaptive segmentation--an exploratory study.

The possible utility of the computer technique of adaptive segmentation in the comparative quantitative characterization of tracé alternant (TA) and REM sleep in the neonatal EEG has been explored in a pilot study of the EEGs of 3 full-term infants of ages 1, 13 and 23 days. The technique of adaptive segmentation, originally developed by its authors for automatically delimiting and characterizing different types of patterns within the same EEG recording, had previously been found effective for this purpose for normal and abnormal adult EEGs. The same computer program parameter values that had been found to be optimal for segmentation of adult EEGs were also found to be optimal for the neonatal ones, as typified by segmentation or demarcation of the bursts and interburst periods in tracé alternant. Adaptive segmentation, in conjunction with clustering of the resulting segments and computation of temporal profiles showing the times of occurrence of the different types of activity in a given phase of sleep together with the mean amplitude and mean frequency of each, supplemented by the respective power spectra, was found to be an effective way of characterizing these EEGs, including certain types of artifact. Further characterization was afforded by statistical summaries of segment durations.

Computers↗
PubMed1985

A general-purpose automatic multichannel electronic switch for EEG artifact elimination.

A general-purpose automatic multichannel electronic switch is described for eliminating artifact from EEG recordings. Although primarily intended for computer processing of EEG data, the device may on occasion be useful in clinical EEG (e.g., during electro-cautery). The switch is activated from a separate artifact-monitoring channel of the electroencephalograph (e.g., accelerometrically monitored head movement, electro-oculographically monitored eye movements or blinks), or from an EEG channel itself, if the artifact in the latter is very marked. Optionally, a sine wave can replace the EEG during the artifact period, and a separate output signal (e.g., for interrupting computer processing) is available for indicating the occurrence of artifact.

Computers↗
PubMed1985

Computerized EEG pattern classification by adaptive segmentation and probability density function classification. Clinical evaluation.

A series of 63 clinical EEGs showing a variety of normal and abnormal patterns was analysed by computer with particular reference to the different types of pattern within the same EEG. Boundaries between different patterns were established by means of adaptive segmentation, so that the duration of the resulting segments was determined by the particular EEG itself (thus the term 'adaptive'). Four channels from each EEG were analysed, paired (left and right) channels were simultaneously segmented and analysed interactively. Similar segments were then clustered without supervision by estimating a probability density function in a 2-dimensional 'feature space' having dimensions of mean frequency and mean power. Individual clusters emerged as well-defined peaks of the surface, individual segments or small groups of duration insufficient to constitute a separate cluster, being identified as 'singular events' (e.g., rare sharp waves, artifacts). The autocorrelation function was used to characterize the EEG both for the segmentation and for the subsequent clustering of the resulting segments. In confirmation of our previous work, adaptive segmentation based on the autocorrelation function of the EEG was found to be quite satisfactory. Unsupervised clustering by estimation of the probability density function in feature space was found to give the correct number of clusters (usually less than 5) in a majority of the records (65%), but in the remaining minority of cases (35%), either overclustering or underclustering occurred. Further, the 'singular events' were occasionally partly included in a formal cluster. Comparison of these results of EEG clustering by unsupervised probability density function estimation with earlier results obtained by supervised hierarchical clustering suggests that there may be subtle cues used by the electroencephalographer in the classification of EEG patterns which have not been adequately approximated by the computer algorithms thus far used in this work. Hence at least some minimal degree of supervision in the clustering process may be necessary, at least for the present. On the other hand, the method recommends itself for the representation of illustrative EEG summaries which, in conjunction with a short written report, would provide the clinical neurologist with a sufficient picture of the real EEG without, in most cases, the need to inspect the original record.

Action Potentials↗
PubMed1985

Moments of the power spectral density estimated from samples of the autocorrelation function (a robust procedure for monitoring changes in the statistical properties of lengthy non-stationary time series such as the EEG.

Accurate estimates of the statistical moments of the power spectral density (PSD) are obtained without computing the Fourier transform of the associated time series. An innovative analytical procedure is derived which reduces the problem to that of summing a small number of weighted samples of the autocorrelation function (ACF). This result significantly reduces the computational requirements for generating meaningful PSD shape descriptors and thus is especially important in biomedical applications where the cost and effort of monitoring lengthy non-stationary time series is a serious practical limitation. In addition the procedure is robust and therefore can be rigorously applied to any stochastic process to estimate its fundamental statistical properties.

Adult↗
PubMed1985

Methods of analysis of nonstationary EEGs, with emphasis on segmentation techniques: a comparative review.

Methods for analysis of nonstationary EEGs, that is, EEGs whose patterns undergo changes with time (e.g., alpha blocking, paroxysmal slow waves, onset of drowsiness/sleep, but excluding spikes/sharp waves) are reviewed. The concepts of stationarity and nonstationarity, and general techniques for their evaluation, are discussed. Simpler methods for monitoring for nonstationarity include running determinations of average amplitude and average period or interval. Piecewise stationary analysis includes characterization, by spectra obtained by fast Fourier transform or by autoregressive modeling, of sections of EEGs preselected to be stationary. In Kalman filtering, the autoregressive model itself becomes time-varying. Segmentation of the EEG into stationary lengths can be carried out on a fixed-interval basis (i.e., of successive, e.g., 1-s intervals), with clustering (grouping) or classification according to the features (e.g., spectra) of each interval, and concatenation of adjacent similar intervals. Alternatively, in adaptive (variable-interval) segmentation, the EEG is continuously monitored automatically for any significant departure from stationarity, and segment boundaries are placed accordingly. A number of applications of the various methods are included, with examples of succinct summary displays. Problems and prospects are discussed.

Algorithms↗
PubMed1984

EMG artifact minimization during clinical EEG recordings by special analog filtering.

A multichannel analog filter for minimizing EMG artifact in routine EEG recordings is described. A 20-channel readily portable version has been implemented which can be easily interfaced with standard electroencephalographs at the auxiliary input/output connectors, in such a way that the filter can be inserted or removed for all channels simultaneously, or, selected channels can be filtered individually. That such an on-line filter be explored was suggested by the remarkable results for minimizing EMG artifact from EEG recordings at seizure onset by off-line computer-based digital filtering recently reported by Gotman, Ives and Gloor. A 4-pole Butterworth filter, i.e., having a maximally flat amplitude response in the passband and having a cut-off frequency, i.e., 30% attenuation point, of 12.5 Hz was selected, the same cut-off frequency as that of the digital filter used by Gotman et al. With this cut-off frequency, which represents a compromise, residual EMG artifact is relatively small. With higher cut-off frequencies, EMG artifact can appear as pseudo-beta activity. Minimization of pseudo-beta activity is obtained, however, at the expense of the elimination of true beta activity. On the other hand, activity of alpha frequencies or lower can be readily identified in the filter output, even with severe EMG artifact in the unfiltered EEG. Other problems in EMG filtering are discussed. Since the analog filter has a non-linear phase characteristic, in contrast to the linear phase characteristic of the finite-impulse-response digital filter used by Gotman et al., the possibility existed that distortion of the wave form of EEG spikes could result, as well as attenuation of their amplitudes. However, the wave forms of filtered spikes in the write-out of the analog filter were found to be essentially identical with those from a one-channel on-line finite-impulse-response computer-based filter having a linear phase characteristic. This finding indicates that phase shift in the analog filter is not a problem, at least for clinical EEG.

Electroencephalography↗
PubMed1984

Analysis of EEG changes with carotid clamping by selective analog filtering, matched inverse digital filtering and automatic adaptive segmentation: a comparative study.

A comparative evaluation was made of 3 methods for possible use in a system for automatic evaluation and signaling of significant EEG changes associated with carotid clamping. The methods were: (1) selective analog filtering in two frequency bands ("fast'--4-20 c/sec; and "slow'--0.8-1.0 c/sec); (2) inverse digital filtering, a computer-based method in which a filter is formed from a pre-clamping EEG baseline such that only frequency components different from the baseline components appear in the filter output; and (3) automatic adaptive segmentation, a computer-based method of detecting and signaling EEG changes by comparison of the autocorrelogram of a pre-clamping reference sample with that from an equal-duration sample "seen' through a progressively moving window in the post-clamping period. For the analyses, portions of ink-recorded monitoring EEGs previously interpreted as showing changes consequent to carotid clamping were transferred to a cassette tape recorder system by means of a multichannel photo-optical scanning system. Of the 3 methods, automatic adaptive segmentation was clearly the best. For selective analog filtering, the thresholds for a significant change were difficult to set. Inverse digital filtering was unsatisfactory because of its insensitivity to changes in the amplitude frequency components already present, although new frequency components could be detected. An incidental finding was that a slow-speed write-out of the analog filter outputs provided a good visual indication of EEG changes.

Brain↗
PubMed1983

Muscle spike artifact minimization in EEGs by time-domain filtering.

An electronic circuit is described for minimizing or eliminating muscle artifact in EEGs. The principle of operation of the circuit is that of a relatively high frequency (250 Hz) sample-and-hold procedure which is interrupted for the duration of a muscle potential. Detection of the presence of the latter is based on a steepness criterion, with a manually adjusted threshold. The circuit is primarily effective for repetitive single muscle spikes. It is not effective if there is a continuous muscle interference pattern, but it can still be useful if the muscle interference pattern is not truly continuous. Very sharp EEG spikes may also be eliminated or at least altered, but less sharp EEG spikes can be passed with little or no change in their wave form.

Action Potentials↗
PubMed1981

Eye movement artifact nulling in EEGs by multichannel on-line EOG subtraction.

A multichannel nulling or subtraction technique for minimizing or eliminating vertical and/or horizontal eye movement artifact including blinks in EEG recordings is described. The circuit, which makes use of principles previously described by other workers, is intended to function integrally with contemporary electroencephalographs, and is relative simple in design and easy to implement.

Electroencephalography↗
PubMed1981

Automatic adaptive segmentation of clinical EEGs.

A method of automatic adaptive segmentation of EEGs, whereby the boundaries between different patterns of activity appearing in a given channel are identified and demarcated, has been applied to a group of clinical EEGs. The EEGs were especially selected from a library of 70 recordings so as to include a diversity of normal and abnormal EEG patterns. In the subsequent steps of the analysis, like segments were automatically clustered and a dendrogram plotted, from which the principal clusters or types of activity were visually selected. For the latter, a temporal profile was then plotted, indicating which type of activity was present at any given time during the respective recording. Summary parameters of mean amplitude and a measure of mean frequency were plotted alongside the temporal profile. The findings are as follows. (1) A single set of segmentation parameters was found to be clinically satisfactory for the entire group. (2) The inappropriateness of adaptive segmentation for the isolation of spikes and sharp waves, which had been anticipated in view of the short duration of such transients in relation to the length of the window (1.2 sec) used for the autocorrelation functions employed in the segmentation algorithm, was confirmed. A separate spike/sharp-wave detection algorithm is therefore planned. (3) Longer transients (i.e., some 300 msec or greater) are segmented and separately clustered. (4) For an individual EEG, the number of clinically significant clusters was 5 or less. (5) If a given cluster included activity in more than one frequency band (for example, simultaneous alpha and slow activity) the simple summary parameters of mean amplitude and a measure of mean frequency may not be sufficient for a clinically adequate description; however, the original autocorrelation function, or, more conventionally, the power spectrum (which can be estimated from the autocorrelation function) provides the necessary information. (6) The use of automatic adaptive EEG segmentation minimized human bias in the selection of portions of EEG recordings for computer analysis.

Computers↗